Search equalizer

ABSTRACT

A method for processing query data is described that includes receiving a query portion from a client over a network. For each of multiple search contexts, a relevance score is determined, based on the query portion. Each search context corresponds to a different set of information against which queries can be executed. Indication of the relevance scores is provided to the client over the network. Determining the relevance score and providing indication are performed prior to an input indicating a complete query or in response thereto. The method may also include associating shortcuts with search contexts, selecting a set of shortcuts based, at least in part, on the relevance scores for the search contexts and the association between the shortcuts and search contexts, and sending the set of shortcuts to the client. The shortcuts include links for accessing a content location associated with the shortcut.

PRIORITY CLAIM

This application claims benefit under 35 U.S.C. §120 as a Continuationof application Ser. No. 11/486,818, filed Jul. 14, 2006, now U.S. Pat.No. 8,301,616, the entire contents of which is hereby incorporated byreference as if fully set forth herein. The applicant(s) hereby rescindany disclaimer of claim scope in the parent application(s) or theprosecution history thereof and advise the USPTO that the claims in thisapplication may be broader than any claim in the parent application(s).

TECHNOLOGY

The present invention relates generally to search engines. Morespecifically, embodiments of the present invention relate to interactiveuser interfaces functional with a search engines for obtaining morerelevant search results.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Search engines are common and useful tools for searching the Internetfor any type of information that is web accessible. They respond to userqueries by generating a list of links to documents deemed relevant tothe query. Search engines are also used in proprietary websites tosearch for information specifically pertaining to the proprietarywebsites.

However, search engines perform all their work for a user only after theuser has entered a query into a query field and issued the query byclicking “Search,” “Enter,” “Go” or another more or less familiarinitiating input. This traditional approach is intuitive because theonly time a search engine “knows” for certain what a user desires iswhen the user decides that the query is correct and complete by formallyissuing the query. Thus, search engines do not provide help to the userwhile the user is formulating a query. Accordingly, search engines must“wait” to provide the search results until the user has determined thatthe query is complete, at which time the user explicitly issues thequery to a search engine. As a logical extension, any additionalinformation relating to the query and search results is provided afterthe user issues the query.

Furthermore, the manner in which the user issues subsequent queries isrelatively time consuming. If the user is dissatisfied with the searchresults of a particular query, the user must reformulate a subsequentquery and then issue that query. Again, the search engine does notprovide any assistance or search results until after the subsequentquery is issued.

Moreover, a number of modern search engines and/or web sites thatprovide access thereto are arranged with or make available to usersthereof multiple verticals, which organize the presentation andavailability of information and sources thereof into groups that conformto some logical arrangement. The Yahoo™ web site, available at theUniform Resource Locator (URL) http://www.yahoo.com for instance, makesavailable to its users at least seven verticals. Users may access theWeb (e.g., the World Wide Web, aka “www”) directly for running searchesthereon using Yahoo's “Web” vertical. The Yahoo web site also makesavailable verticals labeled “Images,” “Video,” “Audio,” “Directory,”“Local,” “News,” and “Shopping.” These verticals serve as intuitivelycomprehensible categories with which users can more closely focus inseeking information. Similarly, the Google™ web site, available at theURL http://www.google.com, provides another familiar example. The Googleweb has verticals named “Web,” “Images,” “Groups,” “News,” “Froogle™” (ashopping related vertical), “Maps,” and “Desktop.”

Although verticals available through the Yahoo, Google and similar webpages as well as more specialized web sites or web sites with a morenarrow or specialized appeal allow users to focus their informationgathering, a significant number of users behave as though they areunaware of these features, unsophisticated about using verticals and/orunsure of which verticals to use. Thus, many users simply avoid seekinginformation therewith and tend to simply perform a Web based search,e.g., a query directed across the Wed, in its entirety. At least inpart, this seems to be a result of the familiarity most users have tothe earlier developed and very well known Web search.

Searching the entire Web in this manner, users tend to obtain largenumbers of “hits” (e.g., query results) relative to searches performedusing the verticals to focus and streamline their search. However, whilethese users tend to obtain a relatively large number of hits using anoverall Web search, all of the many results they obtain therefrom maynot be fully relevant to their purposes. This places users in a positionwherein they must somewhat carefully read through their search results,sometimes over multiple web pages full of hits, many of them lacking inreal relevance. This practice can be tedious, time consuming, costly andprone to errors, in the sense of glossing over or skimming past a trulyrelevant result, which may be obscured, occluded, obfuscated andcamouflaged by the plethora of their other search results, many if notmost of which may be irrelevant to their true search purposes.

Sometimes this causes users to conduct one or more subsequent queries.In doing so, users may modify their query with changed or additionalsearch terms, Boolean search additions and/or groupings such asquotation mark setoffs, conjunctive and/or disjunctive grouping symbolsand the like. However, repeating queries demands time, effort andbandwidth themselves and thus bear associated costs of their own. Thesecosts are intensified by the thoughtfulness users must apply to modifytheir query terms. And obviously, successive searches all make their owndemands on networking and computational resources, consuming time,bandwidth, processing and memory.

One approach to addressing these issues has been for web site hosts toexperiment with associating a graphical indication feature to links forone or more to their verticals, which can indicate a frequency of queryresults obtained that pertain to particular verticals for a given,wholly executed query. However, the indications so provided are providedupon completion of the query's execution static and are thus static.Further, some such indicators seem to ascribe an inference that hitsbelong in certain verticals from results obtained by searching theentire Web. Their usefulness is limited because of their static natureand/or because their accuracy in predicting relevance of the searchresults and/or precision in ascribing the results to a truly relevantvertical.

Based on the foregoing, there is a need for search engines toresponsively and proactively suggest verticals in which a user's querymay be more fruitful in terms of relevance of results and while activelyassisting users with their queries as they formulate them and before theuser formally issues a full and complete query.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 illustrates a user interface that displays predicted queriesbased on input entered in a query field, and search results from themost likely predicted query, according to an embodiment of theinvention;

FIG. 2 is a flow diagram that illustrates how temporal relevance isfactored into determining which potential queries become predictedqueries sent to the user, according to an embodiment of the invention;

FIG. 3A is a block diagram that illustrates the communication between aweb browser on a client and a front end server, according to anembodiment of the invention;

FIG. 3B is a block diagram that illustrates the communication between aweb browser on a client and a front end server, according to anotherembodiment of the invention;

FIG. 3C is a block diagram that illustrates the communication between aweb browser on a client and a front end server, according to anotherembodiment of the invention;

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented;

FIG. 5A depicts a flowchart for an example process for processing querydata, according to an embodiment of the invention;

FIG. 5B depicts the determination of the relevance score based on aquery portion for each of multiple search contexts, according to anembodiment of the invention;

FIG. 5C depicts the provision of the relevance scores to the client overthe network, according to an embodiment of the invention;

FIG. 6 depicts an example system for processing query data, according toan embodiment of the invention;

FIG. 7 depicts an example process for categorizing queries, according toan embodiment of the invention; and

FIG. 8A-8E depict example screen shots showing multiple verticals withquery relevance thereto graphically ascribed, according to an embodimentof the invention.

DETAILED DESCRIPTION

An interactive search equalizer is described herein. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, that the presentinvention may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to avoid unnecessarily obscuring the present invention.

Overview

An interactive search equalizer is described herein. The interactivesearch equalizer described herein categorizes user queries into one ormore verticals and assists a user in determining which of the verticalsprovides more fertile ground for obtaining the most relevant possibleresults for a given query. The search equalizer also provides a choiceto the user for navigating through the various verticals. The user cannavigate through suggestions rendered with the search equalizer and findqueries that may better suit their needs and preferences. In oneembodiment, graphical indicators that can be implemented for instancewith colored, grey scale or otherwise representational bars or othergraphical symbols. The graphical indicators are displayed next to therepresentation of each of the verticals and readily and logicallysymbolize to the user a degree of relevance of each of the verticals forthe user's current query. Graphical relevance representation comprises,in one embodiment, a positioning or array of the verticals in which theposition connotes the relative relevance. For instance, the mostrelevant vertical is displayed above all the others with the next mostrelevant vertical positioned directly below it and so forth, down to theleast relevant vertical displayed below all the others.

In one embodiment, a method for processing query data includes receivinga query portion from a client over a network. For each of multiplesearch contexts, a relevance score is determined, based on the queryportion. Each search context corresponds to a different set ofinformation against which queries can be executed. Indication of therelevance scores is provided to the client over the network.

Determining the relevance score and providing indication are performedprior to an input indicating a complete query or in response thereto.The method may also include associating shortcuts with search contexts,selecting a set of shortcuts based, at least in part, on the relevancescores for the search contexts and the association between the shortcutsand search contexts, and sending the set of shortcuts to the client. Theshortcuts include links for accessing a web based entity associated withthe shortcut. Relevance scores for each search context are determinedfor the World Wide Web and for each of multiple category-significant webportals.

The query portion includes a character string. Upon receiving an inputof the character string, the indication provided includes ranking thecategory-significant web portals according to the relevance scoresdisplaying them with the client. The method can also include, upon thereceiving and/or determining, generating a suggested query andpresenting the suggested query with the client. The suggested query hasa significant proximity to content and/or context of informationassociated with the query portion.

The indication includes a graphical representation of each relevancescore. Providing the relevance score indication can include displayingthe graphical representation with the client. The graphicalrepresentation comprises a graph of each of the relevance scores as afunction of their associated search context.

Therefore, the tedium, time demand, costs and error probabilityassociated with conventional search techniques are obviated, as are theconventionally common need to repeatedly modify search queries, whicheconomizes on networking and computational resource usage. Users of theembodiments described herein enjoy benefits relating to obtaining queryresults that have inherently greater relevance, relevant query resultsbased on portions of queries and suggested queries generated that mayassist the user in obtaining needed relevant information faster and withless intellectual effort.

Embodiments of the present invention relate to an interactive searchequalizer. The interactive search equalizer is described with referencefirst to an example interactive search engine with which the searchequalizer, in one embodiment, is implemented. The interactive searchengine is described first in Section I with reference to FIG. 1-4. Theinteractive search equalizer is described more specifically in SectionII with reference to FIG. 5A-8E. However, it is to be emphasized thatthe interactive search equalizer may be implemented with other searchengines besides that described in Section I.

Section I An Example Search Engine Interface

In one embodiment, the interactive search equalizer is implemented withthe example search engine interface described in this section. However,the search engine interface is described in this section by way ofillustration and not in any way by limitation. Embodiments of thepresent invention, an interactive search equalizer, are well suited toimplementation with various other search engine interfaces.

The example interface to a search engine assists the user 1) bypredicting what the user is searching for based on the character orcharacters the user has entered so far in the query field of theinterface, and 2) by providing search results to the user via theinterface without the user having to formally issue the intended query.For each character entered into the query field, that portion of thequery entered so far is automatically issued to a query predictor thatdetermines a set of one or more predicted queries that correspond to theportion of the query. The set of predicted queries is determined basedon the frequency of previously issued queries alone or also on when theprevious queries were issued. The most likely predicted query isprocessed by a search engine to obtain search results. Both thepredicted queries and the search results are provided to the user viathe interactive user interface. The predicted queries, when displayed tothe user, may be ordered based on their popularity (frequency-based)alone or also on their temporal relevance (time-based).

If the user is not interested in the search results based on the mostlikely predicted query, the user may select any query in the set ofpredicted queries. When the user selects a different predicted query inthe list, the search results are then updated to display the searchresults that pertain to the different predicted query.

In addition to displaying the predicted queries and search results tothe user via the user interface, other dynamic data may be provided thatrelate to the most likely predicted query but are not necessarilyobtained by the search results, such as advertisements and other relatedlinks to websites.

Functional Overview

FIG. 1 illustrates a user interface display, predicted queries, andsearch results, according to one embodiment of the invention. A user,via a web browser 100, enters characters, which will constitute theuser's intended query, into a query field 102. As soon as the firstcharacter is entered, and for every subsequently entered character, thatportion of the intended query is sent to a query predictor, describedbelow (e.g., FIG. 3A-C). The query predictor determines a set of one ormore predicted queries based on the partial query. The predicted queriesare sent back to the user and displayed, for example in a drop box 104.The web browser 100 also displays a selected predicted query 108(hereinafter referred to as the “particular predicted query”).

A search engine processes the particular predicted query 108 from theset of predicted queries and sends the search results 112 to the user tobe displayed, for example in results page 110. It is thus possible thatthe user only has to enter one or a few characters before the actualintended query is determined and the results of the intended query aredisplayed. Therefore, search button 106 may never have to be selected inorder for an intended query to be issued.

Query Predictor

In one embodiment of the invention, the portion of the query entered sofar by a user is sent from the user's web browser to a query predictoracross a network. This may occur for each character, or sequence ofcharacters, entered by the user. The query predictor examines the one ormore characters and makes one or more predictions on what the intendedquery is. The one or more predictions are in the form of one or morecompleted queries, each of which was a previously issued query. Theseone or more predictions are sent to and displayed on the user'scomputer; effectively assisting the user in formulating a query beforethe user is finished typing the entire intended query in the queryfield.

The basic assumption behind the query predictor is that it is highlyprobable that a user intends to issue a query in which at least oneother person has issued previously. By using that information, a highlyinteractive search engine may assist the user in formulating a query, orperhaps aiding the user in refining a query by listing other possiblevariants of the query that the user would be interested in Everypreviously issued query is saved and logged because, if the query wasvaluable to one user, it has potential value for another user.

In one embodiment, the query predictor extends to other languages and isnot exclusive to English. The query predictor may also support othertypes of strings, such as product names and part numbers where the usermay only know a small piece of it.

“Smart” Lexical Completion

The query predictor thus has a searchable database of queries that thequery predictor may access once the query predictor has received one ormore characters from the user. Based on the partial query, the querypredictor determines one or more completed queries from the database ofqueries that match lexically. However, instead of simply completing thepartial query lexically and returning only those queries that begin withthe character or characters in the partial query, other queries are alsofound that contain the lexical completion anywhere in the predictedquery. For example, if a user enters the string “th”, not only may“theory of evolution” be a predicted query sent to the user, but also“string theory” or “music theory,” each of which are not simple lexicalcompletions of “th.”

Frequency and Time

In some situations, many previously issued queries may begin with “th.”It has been determined that the most useful queries are likely the onesthat not only have issued most often (popularity), but also those thathave been issued most recently (temporal relevance). Therefore, in oneembodiment of the invention, the query predictor biases the resultingset of predicted queries based on their frequency (i.e., number of timesthe query has issued in the entire query database history), and howoften they were issued within a specified time, for instance, within thepast week. The fact that the most recently issued queries are biased isbased on the premise that a user is more likely to be interested in asubject that many other people are interested in at roughly the sametime.

As an example, although “renewable energy sources” may have issued as aquery five times more often than “nuclear energy,” the partial query“ener” will cause the query predictor to generate “nuclear energy” asthe particular predicted query because “nuclear energy” may have beenissued much more frequently in the last week due to a hypotheticallyrecent announcement by Congress that 100 nuclear reactors will beconstructed.

In one embodiment, the time component is determined by searching atleast two databases, one for relatively recent queries and one forrelatively older queries, and then scaling the results from searchingthe recent database and weighting them accordingly. FIG. 2 illustratessteps in which this embodiment may be implemented. It will be apparentthat there are many ways this scaling and weighting may be performed, inaddition to the number of “old” query databases and “new” querydatabases, as the invention is not limited to this particular example.In this embodiment, the query predictor has access to a small databaseof all queries that issued in the last week and to a large database ofall queries that issued before a week ago. When searching the smalldatabase for potentially valuable predicted queries, the number of timesa potential query is found in the small database is scaled based on afactor. This factor is the ratio of the number of times a moderatelypopular query is found in the large database to the number of times thatsame moderately popular query is found in the small database. Forexample, suppose that “Yahoo” is a moderately popular query over thelast week and over the past few years. If “Yahoo” is found in the largedatabase 1.7 million times, and 25 thousand times in the small database,then the factor would be 1.7 million/25 thousand, or 68.

Query prediction would be less effective if a moderately popular queryin both the small and large databases were not used to scale. If a querywas popular only in the large database and but not in the smalldatabase, then the scaling factor would be skewed. For example, if thequery “floppy disk” were used as the scaling factor and it was queriedmany times in the history of the large database but was queried only afew times in the previous week, for the simple reason that no oneproduces or uses floppy disks anymore, then the ratio between the largeand small databases would be enormous. This would skew the results of apartial query by heavily weighting relatively recent queries to thedetriment of relatively older, and potentially more valuable, queries.

A similar problem would exist if a new query was used as the scalingfactor that was only issued in the past week but rarely issued in thehistory of the large database. For example, “nuclear energy” may be aninfrequently issued query in the past. But, because of a hypotheticallyrecent announcement by Congress that 100 nuclear reactors will beconstructed, the query “nuclear energy” will likely be issued thousands,if not hundreds of thousands of times. In that case, the scaling factorwould be quite small; and when a query in the small database is weightedagainst the queries in the large database, then relatively olderpredicted queries, rather than relatively newer, and potentially morevaluable, predicted queries, would most likely be returned to the user.

Therefore, referring to FIG. 2, after the query predictor determines, instep 202, the number of times a given potentially valuable query wasissued in the small (i.e. recent) database, the number is scaled, instep 204, by 68, which is based on the scaling factor determined abovewith “Yahoo” as the scaling query. The resulting scaled valueessentially indicates that the potential queries in the small databaseare equal in weight to the potential queries in the large (i.e. old)database. Subsequently, the query predictor determines, in step 206, thenumber of times the potential query appears in the large database of“older” queries.

At this point, a weight is applied to the potential queries in the smalldatabase versus the potential queries in the large database. This isperformed by multiplying the result of the scaled small database numberby ⅔ and adding it to the result of multiplying the number of times thepotential query was found in the large database by ⅓ (see steps208-212). Steps 202-212 are performed for each potential querydetermined by the query predictor. When there are no more potentialqueries to process (214), all the potential queries are then comparedwith each other (step 216) based on their respective values determinedfor each potential query at step 212. The two or more queries (e.g.,ten) with the highest values become the predicted queries, which aresubsequently sent to the user.

Search Engine

In one embodiment of the invention, the search engine componentprocesses the particular predicted query (i.e. the most likely intendedpredicted query) that a user would be interested in. The particularpredicted query is processed to obtain search results. The search enginethat may be used for this purpose is common in the art and requires nofurther description.

The search results obtained by the search engine are sent to anddisplayed on the user's computer. If the particular predicted query isthe user's intended query, the search results based on the particularpredicted query may appear on the user's monitor even before the userenters another character in the query field and very likely before theuser finishes entering the full intended query. If the particularpredicted query is not the user's intended query, then the user mayselect a different predicted query in the list or continue typing, atwhich time a new set of search results, based on the selected or newparticular predicted query, will be displayed via the user interface.

Providing Predicted Queries and Search Results

FIG. 3A is a block diagram that illustrates one way a partial query isprocessed and how the results of the partial query are returned,according to one embodiment of the invention.

A user at a client 300 enters a partial query in a web browser 302. Thepartial query 312 is sent to a front end server 304 over a network 350.Front end server 304 is not a necessary element in any embodiment of theinvention. Its main purpose is to add security to the interactive searchengine system. Network 350 is also not a required element in anyembodiment, but is merely illustrated to show one approach in which theinvention may be implemented. Network 350 may be a local area network(LAN), a wide area network (WAN), or the Internet. Front end server 304forwards partial query 312 to a query predictor 306, discussed above,which processes the partial query.

Front end server 304, query predictor 306, and a search engine 308, orany combination thereof, may be implemented on the same device. However,for the purpose of illustration and simplification, they each reside ondifferent devices.

Query predictor 306 determines a set of one or more predicted queriesbased on the partial query and sends them 314 back to front end server304. Along with the set of predicted queries, query predictor 306 sendsadditional data indicating which of the predicted queries in the set isthe particular predicted query. Either query predictor 306 determineswhich predicted query is the particular predicted query or web browser302 is given sufficient information to make that determination. Frontend server 304 then forwards the predicted queries 314 and the dataindicating the particular predicted query to client 300 over network 350to be displayed on web browser 302.

Upon receipt of the set of predicted queries, web browser 302 sendsparticular predicted query 316 over network 350 to front end server 304,which forwards particular predicted query 316 to search engine 308.Search engine 308, described above, processes the particular predictedquery to obtain search results. The search results 318 are finally sentto front end server 304, which forwards them 318 to client 300 overnetwork 350.

One advantage of this implementation is that the predicted queries aresent immediately to the user as soon as they are determined. However,this implementation also illustrates the possibility that for everycharacter the user types into the query field of his web browser, thereare two complete round trips that a communication has to make betweenclient 300 and front end server 304.

FIG. 3B is a block diagram that illustrates a different way in which apartial query is processed and how the results are returned to the user,according to another embodiment of the invention.

A user at client 300 enters a partial query in a web browser 302.Partial query 312 is sent to front end server 304 over a network 350.Front end server 304 forwards partial query 312 to query predictor 306,which processes the partial query.

Query predictor 306 determines a set of one or more predicted queriesbased upon the partial query and sends them 314 to front end server 304.Instead of immediately forwarding the predicted queries to client 300,front end server 304 retains the predicted queries and sends searchengine 308 the particular predicted query 316. Again, along with the setof predicted queries, query predictor 306 sends additional dataindicating which of the predicted queries in the set is the particularpredicted query. Either query predictor 306 determines which predictedquery is the particular predicted query or front end server 304 is givensufficient information to make that determination.

Search engine 308 processes the particular predicted query to obtainsearch results. The search results 318 are sent to front end server 304,at which time front end server 304 forwards both predicted queries 314and search results 318 to client 300 over network 350.

In the absence of front end server 304, query predictor 306 sends thepredicted queries 314 and to search engine 308, which subsequently sendsthe predicted queries 314 and search results 318 to client 300 overnetwork 350.

One advantage of this implementation is that there is less communication(i.e., traffic) between client 300 and front end server 304. However,the predicted queries may not display on the user's web browser 302 asquickly as in the previous embodiment because the predicted queries must“wait” for the search results to be produced and sent to front endserver 304 before the predicted queries are forwarded to client 300.

FIG. 3C is a block diagram that illustrates a different way in which apartial query is processed and how the results are returned to the user,according to another embodiment of the invention.

A user at client 300 enters a partial query in a web browser 302.Partial query 312 is sent to front end server 304 over network 350.Front end server 304 forwards the partial query 312 to query predictor306, which processes the partial query.

Query predictor 306 determines a set of one or more predicted queriesbased upon the partial query and sends them 314 to front end server 304.Again, along with the set of predicted queries, query predictor 306sends additional data indicating which of the predicted queries in theset is the particular predicted query. Either query predictor 306determines which predicted query is the particular predicted query orfront end server 304 is given sufficient information to make thatdetermination.

Instead of “holding on” to the predicted queries, as in the lastembodiment, front end server 304 sends the predicted queries 314 toclient 300 over network 350 and sends particular predicted query 316 tosearch engine 308 at substantially the same time. It is also possiblefor query predictor 306 to send the particular predicted query to searchengine 308 directly.

Search engine 308 processes the particular predicted query to obtainsearch results. The search results 318 are sent to front end server 304,at which time front end server 304 forwards search results 318 to client300 over network 350. In the absence of front end server 304, querypredictor 306 sends both the predicted queries 314 and the particularpredicted query 316 to search engine 308, after which search engine 308sends predicted queries 314 and search results 318 to client 300 overnetwork 350.

In the absence of front end server 304, query predictor 306 sends bothpredicted queries 314 and the particular predicted query 316 to searchengine 308, which subsequently sends predicted queries 314 and searchresults 318 to client 300 over network 350.

The advantage of this implementation compared to the embodimentdescribed in FIG. 3A is that there is less traffic between client 300and front end server 304. The advantage compared to the embodimentdescribed in FIG. 3B is that the predicted queries do not have to “wait”for the search results to be produced and sent to front end server 304before the predicted queries are forwarded to client 300. Thus, thepredicted queries are sent immediately upon their production and lesscommunication is required between client 300 and front end server 304.

User Interface

In one embodiment of the invention, as illustrated in FIG. 1, the userinterface includes at least 1) a query field 102 where a user enterscharacters that will constitute the partial query, 2) a drop down box104 that lists the set of one or more predicted queries, 3) a searchresults page 110, and 4) a “Search” button 106. The search button may bein the form of any mechanism that allows the user to select the querythe user enters, in case the user is not satisfied with any of thepredicted queries provided by the interactive search engine. The set ofpredicted queries listed in drop down box 104 may be represented inalmost any other type of user interface element, including, but notlimited to, a text box, list box, menu, or context menu. The userinterface may be viewed using a web browser, such as Internet Exploreror Mozilla Firefox.

In one embodiment, the set of predicted queries are listed, beginning atthe top, in order of the most likely predicted query to the least likelypredicted query.

Modifications

In addition to the user interface, query predictor, and search enginedescribed above, the interactive search engine may be modified in manyways to alter the look, feel, and responsiveness of the searchexperience.

Tabs

For instance, the user interface includes tabs, such buttons or links122 in FIG. 1, wherein the user may select a subsection of possiblequeries and search based on that subsection. With a collection of tabsor “search verticals,” such as “Web,” “Images,” “Video,” and “Shopping,”a user may select different query sets. The data for which the querypredictor is predicting is different based on what the user isinterested in, which data is narrowed by using these tabs. For example,if the user is interested in shopping for a product, the user selectsthe “Shopping” tab. The user then begins to enter a product name orservice in query field 102. The query predictor is not only sent thepartial query but also the shopping selection information, indicatingthat the user is searching for a particular product or service, whereinthe query predictor returns only those predicted queries that pertain toproducts and services.

Keywords

Often when a query is issued, the order of words in the query isunimportant. As alluded to earlier, the issued query does not have to bein English. In other embodiments, not only are other natural languagessupported, but also non-natural strings, such as product names and partnumbers where the user may only know a portion of the non-naturalstring. Therefore, the term “word” as used herein may include an Englishword, a Korean word, or a product number.

When a user enters two or more words in the query field, the user is notnecessarily concerned that the search engine returns a link to a webaccessible document that contains the two or more words in the orderthat they were entered. Rather, the user is interested in a webaccessible document that merely contains those words, in whatever orderthey are found.

For example, a user enters “solar wind water power” in the query field.The user does not particularly care about the order. The user is ratherinterested in queries that contain the words “solar,” “wind,” “water,”and “power” somewhere in the query. The query predictor determines whatwords are important in the query and which words are not important, andthen predicts queries based on the important words instead of predictingqueries based simply on a matching substring.

Delay Results

In another embodiment, the step of displaying the predicted queriesand/or the search results is delayed. Instead of immediately returningpredicted queries, the query predictor “waits” until certain criteria issatisfied (such as the lapse of a specified amount of time or when a fewcharacters are entered, or both) before the predicted queries and searchresults are displayed. This additional step of waiting assumes that theuser may not be sure what he/she wants to query on. Thus, the predictedqueries are delayed until the interactive user interface determines,based on the waiting criteria, that this is what the user truly intendsto query on. Once the waiting criteria are satisfied, the partial queryis processed by the query predictor and the search engine, as describedabove.

Other Dynamic Data

There are additional ways to aid users other than to predict theintended query and return the appropriate search results. In anotherembodiment, advertisements that appear on the interactive user interfacechange based on the particular predicted query returned from the querypredictor. Thus, every time the particular predicted query changes, newadvertisements that relate to the query are posted on the user interfaceand advertisements that related to an older and non-relevant query aredeleted from the user interface. For instance, if a user types “elli”and the query predictor determines “elliptical” as the particularpredicted query, advertisements that relate to exercise equipment willappear on the user interface.

In addition to advertisements, other dynamic information may be usefulto the user when submitting a query. In another embodiment, informationrelating to a particular predicted query but not found in the searchresults are displayed to the user via the user interface. Extending the“theory” example used above, the query predictor determines that“theory” is the particular predicted query for the partial query “th”entered by the user. The query predictor, or perhaps another program,determines that “theory” is associated with “string theory,” “musictheory,” and “math theory” and returns these related subjects to bedisplayed in the form of predicted queries or in a different form on theuser interface. For short queries like “theory,” this additionalinformation happens to be the same set as what the query predictor wouldproduce.

However, if the user entered “interna” in the query field and the querypredictor determined that the particular predicted query is“international trade” then the query predictor, in addition to thepredicted queries, would return queries that are not lexical completionsof “international trade,” but rather queries related to the topic ofinternational trade. Such queries could be on GATT, WTO, UN, US tradepolicies, etc. A program separate from the query predictor could alsoperform this function.

Clearly, this aspect of the invention is not performing queryprediction, but rather is providing the user with dynamic, related, andhopefully helpful information. A principle in providing advertisements,additional queries, and other related information is to keep everythingthat is displayed via the user interface consistent with what the querypredictor “believes” is the user's intent, which the query predictordetermines from the partial query.

Implementation Mechanisms

FIG. 4 is a block diagram that illustrates a computer system 400 uponwhich an embodiment of the invention may be implemented. Computer system400 includes a bus 402 or other communication mechanism forcommunicating information, and a processor 404 coupled with bus 402 forprocessing information. Computer system 400 also includes a main memory406, such as a random access memory (RAM) or other dynamic storagedevice, coupled to bus 402 for storing information and instructions tobe executed by processor 404. Main memory 406 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by processor 404. Computersystem 400 further includes a read only memory (ROM) 408 or other staticstorage device coupled to bus 402 for storing static information andinstructions for processor 404. A storage device 410, such as a magneticdisk or optical disk, is provided and coupled to bus 402 for storinginformation and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

The invention is related to the use of computer system 400 forimplementing the techniques described herein. According to oneembodiment of the invention, those techniques are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from anothermachine-readable medium, such as storage device 410. Execution of thesequences of instructions contained in main memory 406 causes processor404 to perform the process steps described herein. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions to implement the invention. Thus,embodiments of the invention are not limited to any specific combinationof hardware circuitry and software.

The term “machine-readable medium” as used herein refers to any mediumthat participates in providing data that causes a machine to operationin a specific fashion. In an embodiment implemented using computersystem 400, various machine-readable media are involved, for example, inproviding instructions to processor 404 for execution. Such a medium maytake many forms, including but not limited to, non-volatile media,volatile media, and transmission media. Non-volatile media includes, forexample, optical or magnetic disks, such as storage device 410. Volatilemedia includes dynamic memory, such as main memory 406. Transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 402. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Common forms of machine-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punchcards, papertape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of machine-readable media may be involved in carrying oneor more sequences of one or more instructions to processor 404 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.As another example, communication interface 418 may be a local areanetwork (LAN) card to provide a data communication connection to acompatible LAN. Wireless links may also be implemented. In any suchimplementation, communication interface 418 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of carrier wavestransporting the information.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution. In this manner, computer system 400 may obtainapplication code in the form of a carrier wave.

Section II Search Equalizer

A search equalizer is described herein. In Section I above, an exampleinteractive search engine is described with reference to FIG. 1-4 withwhich an embodiment of the present invention, a search equalizer may beimplemented. It should be understood that the interactive search enginedescribed in Section I is but one platform with which the searchequalizer described herein may be practiced and that the interactivesearch engine is described above by way of illustration and not by anymeans limitation. Embodiments of the present invention are well suitedto implement a search equalizer with any of a variety of search engines.

A method for processing query data includes receiving a query portionfrom a client over a network. For each of multiple search contexts, arelevance score is determined, based on the query portion. Each searchcontext corresponds to a different set of information against whichqueries can be executed. Indication of the relevance scores is providedto the client over the network.

Determining the relevance score and providing indication may beperformed prior to an input indicating a complete query or in responsethereto. The method may also include associating tags with searchcontexts, selecting a set of tags based, at least in part, on therelevance scores for the search contexts and the association between thetags and search contexts, and sending the set of tags to the client. Thetags include links for accessing a content location such as a web pageor a similar web based entity associated with the tag. Relevance scoresfor each search context are determined for the World Wide Web and foreach of multiple category-significant web portals.

The query portion includes a character string. Upon receiving an inputof the character string, the indication provided includes ranking thecategory-significant web portals according to the relevance scoresdisplaying them with the client. The method can also include, upon thereceiving and/or determining, generating a suggested query andpresenting the suggested query to the client. The suggested query has asignificant proximity to content and/or context of informationassociated with the query portion.

The indication may include a graphical representation of each relevancescore. The graphical representation may comprise a graph of each of therelevance scores as a function of their associated search context.Providing the relevance score indication can include displaying thegraphical representation with the client.

Example Process

FIG. 5A depicts a flowchart for an example process 50 for processingquery data, according to an embodiment of the invention. Process 50begins with step 51, wherein a query portion is received from a clientover a network. As used herein, a query portion refers to a part of aquery (e.g., a partial query) or complete query. A partial querycomprises a query portion executed essentially in real time as a user isactively inputting a string of characters associated with a query. Acomplete query comprises a query potion executed upon a user input thatindicates that a character string associated with a query portion isessentially fully composed according to the user's satisfaction. Userinputs that connote a complete query include activating an ‘enter’ or‘return’ key on a keyboard or the like and/or an input with a graphicaluser interface (GUI) or another user interface such as clicking on a‘Search’, ‘Go’, ‘Get’, ‘Find’ or similar radio button.

In step 52, a relevance score is determined based on the query portionfor each of multiple search contexts. Each search context corresponds toa different set of information against which queries can be executed. Inone implementation, one set of information is the Web, e.g., the WorldWide Web (popularly referred to as “www”) and other sets of informationare one or more verticals. As used herein, a vertical refers to a searchcontext, a category leading web portal, web site, etc. in a given,particular, relatively non-generalized and/or somewhat more specializedtopical category and/or catering to the informational interests of agiven, relatively non-generalized demographic group. Verticals aredescribed in more detail hereinafter. In step 53, an indication of therelevance scores is provided to the client over the network.

In one embodiment, determining a relevance score and providingindication thereof is performed prior to receiving an input thatindicates that the query portion represents a complete query. Thus, thepresent embodiment advantageously provides the relevance data based on amere string of characters that may comprise a partially formed query andallows suggestions to be provided to users for completing the query, asdiscussed below. In one embodiment, determining a relevance score andproviding indication thereof is performed in response to receiving aninput that indicates that the query portion represents a complete query.This has the benefit of responding with relevance data to a user'swell-formulated query.

In one embodiment, process 50 continues with step 54, wherein shortcutsare associated with search contexts. In one embodiment, shortcutscomprise links (e.g., hyperlinks) to a content location such as a webpage. In step 55, a shortcut is selected, at least in part, based on therelevance scores for the search contexts and the association between thetags and the search contents. In step 56, a set of shortcuts selectedfrom the search context deemed most relevant (e.g., having the highestrelevance score), the top two (or another relatively small number) mostrelevant search contexts, etc., are sent to the client. In oneimplementation, the shortcuts are interactively displayed with theclient to users who may conveniently access the content locations withan input based on the shortcut, such as clicking thereon.

In one embodiment, process 50 continues with step 57. In step 57, arandom query is categorized into various search contexts (e.g.,verticals). Categorizing a random query into various search contexts iseffectuated in one implementation with a general or other queryunderstanding Application Program Interface (API).

FIG. 5B depicts the determination of the relevance score based on aquery portion for each of multiple search contexts of step 52 insomewhat greater detail. In step 521, a relevance score is determinedfor the World Wide Web, essentially in its entirety. In step 522, arelevance score is determined for multiple category-significant webportals, e.g., verticals, etc. Thus, relevance scores may be determinedfor Web based searches and for searches over one or more particularverticals, allowing users to compare and choose between them, whichadvantageously promotes effective and efficient relevant searchcomposition. In various embodiments, step 52 of process 50 comprises oneor more of steps 521 and 522.

FIG. 5C depicts the provision of the relevance scores to the client overthe network of step 53 in somewhat greater detail. In step 531,category-significant web portals such as verticals are ranked accordingto their relevance scores. In step 532, the category-significant webportals are displayed with their corresponding relevance score rankings.In one implementation, the relevance score rankings are displayedgraphically, such as a relevance representative graph appearingproximate to the representation of the vertical or othercategory-significant web portal. Thus, relevance scores may be displayedfor Web based searches and for searches over one or more particularverticals, allowing users to compare and choose between them. This hasthe benefit of promoting effective and efficient selection of searchcontexts for queries that have a higher probability of returningrelevant results. In various embodiments, step 53 of process 50comprises one or more of steps 531 and 532.

In one embodiment, process 50 is performed with one or more processorsexecuting machine readable program code encoded in a tangible computerreadable medium such as described above with reference to FIGS. 3A, 3Band/or 4. In one embodiment, process 50 is performed with one or moreprocessors of components of a network based system executing machinereadable program code encoded in a tangible computer readable mediumsuch as described below with reference to FIG. 6.

Example System

FIG. 6 depicts an example system 60 for processing query data, accordingto an embodiment of the invention. In one embodiment, system 60comprises subsystems and/or components such as those described inSection I with reference to FIGS. 3A-B and 4. System 60 has a network65. Network 65 comprises, in various implementations, one or more of avariety of computer and/or communication networks including a LAN, WAN,subnet, internetwork and/or the Internet. In one embodiment, componentsof system 60 are configured for processing data related to queries andquery portions with program functions that comprise steps such as thoseof process 50, described with reference to FIG. 5A-C.

A client 61 is communicatively coupled to network 65. Client 61 hosts aweb browser application 612 and a user interface such as a GUI 614,which allow users to input queries and portions thereof, such as maytake the form of one or more strings of characters entered in aninteractive text field. Client 61 is coupled via network 65 to a frontend server 64, which receives, handles and processes queries and queryportions therefrom.

A gossip server 66 is communicatively coupled to the front end server 64and generates suggested queries based on query portions. The gossipserver 66 also outputs frequencies of the suggestions for categorizingqueries and ascribing relevance thereto, e.g., as described withreference to FIG. 7 below. Gossip server has buzz logs 663, whichcompiles frequencies, statistics and/or related, similar or other datathat relate to frequencies of suggestions, query suggestions, relevance,etc. In one embodiment, the gossip server 66 comprises an engine such asis described in co-pending U.S. patent application Ser. No. 11/313,525,filed on Dec. 21, 2005 by Richard Kasperski and assigned to the Assigneeof the present application, which to the extent not repeated herein, isfully incorporated herein by reference in its entirety for all purposes.

A search engine 68 is communicatively coupled to front end server 64 andprocesses queries and query portions with functions relating tosearching for information related to the queries and query portions. Inone implementation, search engine 68 is configured for associating tags,which comprise links for accessing an associated content location, withsearch contexts, such as verticals, other portals and/or the World WideWeb. The search engine 68 selects tags based, at least in part, on therelevance scores for each search context and the association between thetags and the search contexts. Search engine 68 then sends the tags toclient 61, e.g., with front end server 64 and network 65. In oneembodiment, search engine 68 has a general (or other)query-understanding API 661. Where the query portion is associated witha random query, API 661 categorizes the query portion into the varioussearch contexts (e.g., verticals). In another implementation, API 661 isdisposed in front end server 64 and/or elsewhere in system 60.

Categorizing Queries Example Process

FIG. 7 depicts an example process 70 for categorizing queries, accordingto an embodiment of the invention. In one embodiment, one or more stepsof process 70 are performed with functionality associated with a gossipserver (e.g., gossip server 66; FIG. 6). Process 70 begins with step 71,wherein the top two suggestions are obtained from the gossip serveralong with the frequencies associated therewith.

In step 72, the gossip server returns the raw frequencies of thesuggestions on a total number ‘N’ of queries. The query population N canbe quite large in some large portal based implementations. For instance,when deployed and used with a large web portal such as Yahoo™, N is anumber of approximately 1.1 billion queries. In step 73, the frequenciesof the top two suggestions are calculated in the remaining searchcontexts, verticals, category leading web portals, web sites, etc., inone embodiment using buzz logs (e.g., buzz logs 663; FIG. 6). Thefrequencies are normalized by a factor ‘f’.

Factor f varies for the different search contexts, verticals, categoryleading web portals, web sites, etc. For a given search context,vertical, category leading web portal, web site, etc.,f=N/(total number of queries in that vertical)  (Equation 1).In one implementation, N is a number much greater than the total numberof queries in a particular vertical, etc. For instance, in one exampleimplementation, buzz logs from a half year's verticals are used. Inanother, a year of logs compiling such data is used.

In step 74, the cumulative weight, rank, etc. of a given search context,vertical, category leading web portal, etc. is calculated. In oneimplementation, the weight/rank of the given search context, vertical,category leading web portal, etc. is calculated by attaching weight(e.g., ascribing numerical significance) to the two top suggestionsbased on their respective frequencies. The cumulative weight of the twotop suggestions is calculated in one embodiment according to Equation 2,below.w _(cum) =f1*(f1/(f1+f2))+f2*(f2/(f1+f2))  (Equation 2).The weights attached to the suggestions are proportional to theirfrequencies. This ensures that proper authority is attached to each ofthe suggestions and deters bias in favor of any.

In step 75, the verticals are ranked (e.g., sorted) based on theircumulative weight numbers. In one embodiment, the gossip servercomprises an engine described with reference to gossip server 66 (FIG.6) above.

Example Screen Shots

FIG. 8A-8E depict example screen shots showing multiple verticals withquery relevance thereto graphically ascribed, according to an embodimentof the invention. FIGS. 8A and 8B depict a screen shot 81 of an examplewindow as rendered with web browser 612 on a display monitor associatedfor instance with client 61 and allowing user interaction with GUI 614(FIG. 6). Web indicator 811 (“Web 811”) and vertical indicators 812(“Verticals 812”) depict various search contexts.

Web indicator 811 shows a search context that essentially includes theWorld Wide Web in its substantial entirety. Vertical indicators 812 showeight example search contexts as verticals (e.g., category leadingand/or specific web portals), however, any number of verticals can beshown. Relevance indicators 815 graphically display the relativerelevance of each of search contexts 811 and 812.

A query portion 814 reading (as an example) “anime wallpaper” has beenentered by a user into the text input field shown, below which suggestedquery 813 that has been generated in response to entering each characterof the character string comprising the query portion 814. The suggestedquery reads (as an example) “ . . . wallpaper.” The relevanceindications in relevance indicators 815 and/or the suggested query 813change dynamically as the character string is added on the fly to by theuser in composing the query portion 814. This provides the user withbeneficial real time relevance input and query suggestions based on thequery portion 814. The query portion 814 thus grows dynamically as usersadd to the character string thereof. The query portion 814 can alsocomprise a complete query, which can be initiated with a user inputindicative thereof, such as clicking ‘enter’, ‘return’, and/or a GUIbased radio button feature such as one labeled “search,” “go,” “find” orthe like.

As seen in screen shot 81, the most relevant search context for queryportion 814 is revealed with the relevance indicators 815 to be thevertical “images.” The Word Wide Web is shown to be the next mostrelevant search context, followed by the vertical “Video.” The otherverticals show significantly less relevance. Thus, a user can increasethe probability of greater relevance in query results by running thesearch in the “images” vertical, in contrast to any of the other searchcontexts. However, this relevance array may change with a differentquery portion 814, as may the suggested query 813, etc., as shown inFIG. 8C.

FIG. 8C depicts another example screen shot 82 of an example window asrendered with a web browser on a display monitor associated with aclient and allowing user interaction with a GUI. Web indicator 811 andvertical indicators 812 again depict various search contexts. In FIG.8C, the query portion 814 entered into the interactive text field nowreads “leather bags.” Screen shot 82 shows that the search contexts havedifferent relevance scores indicated with relevance indicators 815 thanin screen shot 81. In screen shot 82, the vertical 812 that correspondsto “shopping” now has heightened relevance indicated. The top two searchcontexts in screen shot 82 are “shopping” vertical 815 and the Webindication 811.

FIG. 8D depicts an example screen shot 83, which has a different formatthan that shown in screen shots 81 and 82. The functions are similar. Inscreen shot 83, an input denoting a complete query can be made byclicking the radio button 888.

FIG. 8E depicts an example screen shot 85, which is somewhat similar informat to screen shots 81 and 82. Screen shot 85 however alsoillustrates multiple shortcuts 873. Shortcuts 893 comprise links (e.g.,hyperlinks) to content locations that are associated with searchcontexts. Upon an input of query 877, Shortcuts 873 are suggested by agossip server from the most relevant search context or contexts. Asillustrated in screen shot, the vertical 875 corresponding to “Images”is ranked as the most relevant of the ranked verticals 875. Thus,shortcuts 873 display links to the most relevant query results suggestedfrom the most relevant of the verticals 875. Query 877 comprises acomplete query in which a user types (or otherwise inputs) the characterstring and activates an ‘enter’, ‘return’ or similar key or a radiobutton for entering a search-initiating input such as a ‘search’, ‘go’,‘find’ or similarly functional button.

In one embodiment, verticals 875 are, upon a user input representativeof a complete query, responsively arranged (e.g., rearranged) in anarray in which their relevance is graphically represented by theirposition in the array, such as with the most relevant ranked verticalsarranged in the uppermost position, followed by the next most relevantvertical and so forth, down to the least relevant vertical, arrangedbelow the other verticals. Thus, the relevance rankings of verticals 875can be graphically display while obviating other relevance rankinggraphical indicators, which can have the benefit of economizing onscreen space and/or clutter.

Extensions, Alternatives and Miscellaneous

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. Any definitions expressly set forth herein for termscontained in such claims shall govern the meaning of such terms as usedin the claims. Hence, no limitation, element, property, feature,advantage or attribute that is not expressly recited in a claim shouldlimit the scope of such claim in any way. The specification and drawingsare, accordingly, to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A method, comprising: generating one or morequery logs that record information about previously received searchqueries, including, for the previously received search queries,indications of in which specific contexts, of a plurality of searchcontexts, the previously received search queries have been made over atleast a period of time; receiving a search query; determining at leastone suggested query, of the previously received search queries, based onthe search query; determining, for each specific search context of aplurality of search contexts: a log-based relevance score of the searchquery to the specific search context, the log-based relevance scorebeing calculated, at least in part, using a count of how many times thesearch query was made in the specific search context over the period oftime, and further using a count of how many times the suggested querywas made in the specific search context over the period of time, asrecorded in the one or more query logs; wherein each search context ofthe plurality of search contexts is a different set of searchableinformation; responsive to receiving the search query, sending anindication, for each specific search context of the plurality of searchcontexts, of a relative size of the log-based relevance score determinedfor the specific search context compared to relative sizes of each otherlog-based relevance score determined for each other search context ofthe plurality of search contexts; wherein the method is performed by oneor more computing devices.
 2. The method as recited in claim 1 whereinthe search query is a partial search query, wherein determining thelog-based relevance scores and providing the indication are performedprior to receiving input that indicates that said search queryrepresents a complete query.
 3. The method as recited in claim 1 whereindetermining each log-based relevance score comprises: using anormalizing function based at least on : (1) a total number of queriesthat have been previously submitted in any of the plurality of searchcontexts over the period of time and (2) a total number of queries thathave been previously submitted in the specific search context over theperiod of time.
 4. The method as recited in claim 1, further comprising:determining two or more suggested queries for the search query, whereinat least a first suggested query of the two or more suggested queries isdifferent from the search query; based on the one or more query logs,determining, for each particular suggested query of the two or moresuggested queries, for each specific search context of the plurality ofsearch contexts, a number of times the particular suggested query wasmade in the specific search context over the period of time; whereineach particular relevance score of the log-based relevance scores isbased at least on the numbers of times determined for the two or moresuggested queries with respect to the specific search context to whichthe particular relevance score pertains.
 5. The method as recited inclaim 1, further comprising, based on the one or more query logs,determining a particular log-based relevance score for a particularsearch context by at least performing one of: calculating a frequency ofhow many times the search query was made in the particular searchcontext over the period of time compared to how many times the searchquery was made in the plurality of search contexts over the period oftime.
 6. The method as recited in claim 1 further comprising: whereinthe indication comprises, for each specific search context of theplurality of search contexts, an indicator of the log-based relevancescore determined for the specific search context, and a shortcut linkfor accessing a content location associated with the specific searchcontext.
 7. The method of claim 1, wherein providing the indicationcomprises providing a graphical representation the log-based relevancescores; wherein the graphical representation comprises a plurality ofbars, each bar being representative of a different score of thelog-based relevance scores, the size of each bar being based on upon thedifferent score.
 8. The method of claim 1, wherein the plurality ofsearch contexts includes one or more of: a web portal or a searchvertical.
 9. The method of claim 1, wherein the one or more query logscomprise at least a first plurality of search request log entries, eachparticular search request log entry of the first plurality of searchrequest log entries logging a specific search request, the particularsearch request log entry indicating a specific query that was receivedfor the specific search request, and search vertical selectioninformation that indicates in which specific search context the specificquery was made.
 10. The method of claim 1, wherein each specific queryrecorded in the previously received search queries was entered in aninteractive text field configured to accept a query directed to acorresponding specific search context and send, to a search engine, thesearch vertical selection information identifying the correspondingspecific search context.
 11. The method of claim 1, further comprisingselecting the plurality of search contexts to include in the indicationfrom a superset of search contexts based on the log-based relevancescores.
 12. One or more non-transitory computer-readable media storinginstructions that, when executed by one or more computing devices, causeperformance of: generating one or more query logs that recordinformation about previously received search queries, including, for thepreviously received search queries, indications of in which specificcontexts, of a plurality of search contexts, the previously receivedsearch queries have been made over at least a period of time; receivinga search query; determining at least one suggested query, of thepreviously received search queries, based on the search query;determining, for each specific search context of a plurality of searchcontexts: a log-based relevance score of the search query to thespecific search context, the log-based relevance score being calculated,at least in part, using a count of how many times the search query wasmade in the specific search context over the period of time, and furtherusing a count of how many times the suggested query was made in thespecific search context over the period of time, as recorded in the oneor more query logs; wherein each search context of the plurality ofsearch contexts is a different set of searchable information; responsiveto receiving the search query, sending an indication, for each specificsearch context of the plurality of search contexts, of a relative sizeof the log-based relevance score determined for the specific searchcontext compared to relative sizes of each other log-based relevancescore determined for each other search context of the plurality ofsearch contexts.
 13. The one or more non-transitory computer-readablemedia of claim 12, wherein the search query is a partial search query,wherein determining the log-based relevance scores and providing theindication are performed prior to receiving input that indicates thatsaid search query represents a complete query.
 14. The one or morenon-transitory computer-readable media of claim 12, wherein determiningeach log-based relevance score comprises: using a normalizing functionbased at least on : (1) a total number of queries that have beenpreviously submitted in any of the plurality of search contexts over theperiod of time and (2) a total number of queries that have beenpreviously submitted in the specific search context over the period oftime.
 15. The one or more non-transitory computer-readable media ofclaim 12, wherein the instructions, when executed by the one or morecomputing devices, further cause performance of: determining two or moresuggested queries for the search query, wherein at least a firstsuggested query of the two or more suggested queries is different fromthe search query; based on the one or more query logs, determining, foreach particular suggested query of the two or more suggested queries,for each specific search context of the plurality of search contexts, anumber of times the particular suggested query was made in the specificsearch context over the period of time; wherein each particularrelevance score of the log-based relevance scores is based at least onthe numbers of times determined for the two or more suggested querieswith respect to the specific search context to which the particularrelevance score pertains.
 16. The one or more non-transitorycomputer-readable media of claim 12, wherein the instructions, whenexecuted by the one or more computing devices, further cause performanceof, based on the one or more query logs, determining a particularlog-based relevance score for a particular search context by at leastperforming one of: calculating a frequency of how many times the searchquery was made in the particular search context over the period of timecompared to how many times the search query was made in the plurality ofsearch contexts over the period of time.
 17. The one or morenon-transitory computer-readable media of claim 12, wherein theindication comprises, for each specific search context of the pluralityof search contexts, an indicator of the log-based relevance scoredetermined for the specific search context, and a shortcut link foraccessing a content location associated with the specific searchcontext.
 18. The one or more non-transitory computer-readable media ofclaim 12, wherein providing the indication comprises providing agraphical representation the log-based relevance scores; wherein thegraphical representation comprises a plurality of bars, each bar beingrepresentative of a different score of the log-based relevance scores,the size of each bar being based on upon the different score.
 19. Theone or more non-transitory computer-readable media of claim 12, whereinthe plurality of search contexts includes one or more of: a web portalor a search vertical.
 20. The one or more non-transitorycomputer-readable media of claim 12, wherein the one or more query logscomprise at least a first plurality of search request log entries, eachparticular search request log entry of the first plurality of searchrequest log entries logging a specific search request, the particularsearch request log entry indicating a specific query that was receivedfor the specific search request, and search vertical selectioninformation that indicates in which specific search context the specificquery was made.
 21. The one or more non-transitory computer-readablemedia of claim 12, wherein each specific query recorded in thepreviously received search queries was entered in an interactive textfield configured to accept a query directed to a corresponding specificsearch context and send, to a search engine, the search verticalselection information identifying the corresponding specific searchcontext.
 22. The one or more non-transitory computer-readable media ofclaim 12, wherein the instructions, when executed by the one or morecomputing devices, further cause performance of: selecting the pluralityof search contexts to include in the indication from a superset ofsearch contexts based on the log-based relevance scores.